wa {paltran} | R Documentation |
This function computes with a given training set and a given environmental parameter a weighted averaging transfer function as used in paleolimnology. For the calculation of the model predicting error 10 fold cross validation, bootstrap ore Leave-on-out can bee chosen. Inverse or classical deshrinking are supported.
wa(..., d.plot = TRUE, env.trans = FALSE, spec.trans = FALSE, diagno = TRUE, val = c("none", "10-cross", "loo", "boot"), run = 10, scale =FALSE, seed = 1,out = TRUE, desh.meth =c("class", "inverse"), drop.non.sig = FALSE,min.occ = 1)
... |
required x,y: a matrix or data frame of the species training set (x) and a vector or data frame of the related environmental parameter (y). optional: core samples (z) - vector or data frame of species data from a sediment core. |
d.plot |
TRUE/FALSE: if TRUE diagnostic plots are given at the end of the analysis. |
env.trans |
Should the environmental parameter bee transformed? Type "sqrt" for square root and "log10" for the logarithm to the basis 10 are possible choices, default is FALSE. |
spec.trans |
Should the species data bee transformed? "sqrt" for square root and "log10" for the logarithm to the basis 10 are possible choices, default is FALSE. |
diagno |
should N2,number of non zero values bee calculated for the training set and test set? Default is TRUE |
val |
validation method: one of "boot" (bootstrap), "loo" (Leave-on-out), or "10-cross" (10-fold cross validation) |
run |
if "boot" or "10-cross" were chosen: number of cycles to run |
scale |
should the data scaled up to 100 percent? (Default is FALSE) |
seed |
set the seed for the random generator (using boot or 10-cross), default = 1 |
out |
should the results printed on the console? |
desh.meth |
what kind of deshrinking method should bee used "class"(classical deshrinking), or "inverse" (inverse deshrinking), default is "inverse" |
drop.non.sig |
should a taxon that have non significant response to the environmental variable bee deleted? The calculation, if there is a significant relation between a taxa and the environmental variable of interest, is undertaken using a generalized additive model (GAM) and the package mgcv. As a GAM only works if a taxon occurred several times, only those taxa will be included that occurred more than 5 times (k=3). |
min.occ |
minimum occurrence: all taxa with less than min.occ will be deleted from the training set |
species in train.set |
Number of non zero species in each sample of the training set |
N2 train.set |
Hill's N2 of each sample of the training set |
species.optima |
wa-optima of each species |
inferred train.set |
inferred environmental parameter for the training set |
performance |
performance of the wa-regression |
species in core.samples |
Number of none zero species in each sample of the core data set |
n species core.samples in train.set |
How many species in the core samples are represented in the training set |
N2 in core.samples |
Hill's N2 of each sample of the core data |
reconstruction_core.samples |
reconstructed environmental parameter for the samples of the core |
inferred train.set.val |
mean inferred environmental parameter for the training set using cross validation |
mean(reconstruction_core.samples).val |
reconstructed environmental parameter for the samples of the core using "boot" or "loo" |
sd(reconstruction_core.samples).val |
standard deviation of the reconstructed environmental parameter for the samples of the core using "boot" or "loo" |
reconstruction_core.samples.val |
reconstructed environmental parameter for the samples of the core for each run of "boot" or "loo" |
Sven Adler
ter Braak, C.J.F. & van Dam, H. 1989. Inferring pH from diatoms: a comparison of old and new calibration methods. Hydrobiologia 178:209-23.
package analogue
data(train_set.MV) data(train_env.MV) data(dud.df) try<-wa(train_set.MV,train_env.MV) try<-wa(train_set.MV,train_env.MV,desh.meth="class") names(try) try<-wa(train_set.MV,train_env.MV,dud.df,val="boot",run=10)